Punctate calcifications play a vital role in the mammographic detection of minimal breast cancer. At the Cincinnati Breast Cancer Detection Center calcifications played some part in detecting all of the minimal cancers found by mammography alone. However, only 15 percent of the biopsies performed primarily because of detected calcifications were found to be cancer. Use of extracted calcification features, that distinguish benign from malignant calcification images, by either trained human observers or computer assisted observers would result in a reduced biopsy rate and an increased yield of minimal, curable breast cancers. The purpose of this project is to determine if an already developed data acquisition, image processing, and pattern analysis computer program can find clinically significant features that distinguish between the radiographic images of calcifications found in benign biopsies. In a preliminary application of this program 23 of 23 malignant calcifications were correctly classified and 6 of 28 benign calcifications were incorrectly classified. In this proposed study this program will be trained and tested on separate sets of 200 calcifications selected one each from 400 individual biopsies. This program will be extended into a two level decision structure to classify biopsies containing from 5 to 20 individual calcifications. This two level decision structure will be trained and tested on separate sets of 50 biopsy radiographs from an additional 100 patients, with multiple calcifications. This phase will require the digitization of approximately 1000 individual calcifications. A data base with over 1400 digitized calcifications from at least 500 patients, along with pathology findings will be created as a generally available resource. Extracted calcification features will be tested for clinical significance. Human observers trained to use these characteristics will be tested against observers trained using presently accepted criteria.